Association of serum creatinine variability and risk of 1-year mortality among patients with cancer

Association of serum creatinine variability and risk of 1-year mortality among patients with cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of serum creatinine variability and risk of 1-year mortality among patients with cancer Lin Li, Huanhuan Yang, Yi Zhang, Jianchao Liu, Shunfei Li, Lijun Wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4639262/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Creatinine variability has a close and reciprocal relationship with cancer risk. However, the role of creatinine variability on mortality among cancer patients remains unclear. Thus, the objective here is to fill this gap. We conducted a multi-center study including all patients with solid tumors admitted to eight hospitals in China between January 1, 2013, and December 31, 2019, on their primary admission. The variability of blood creatinine was evaluated by the standard deviation (SD) and coefficient of variation (CV) . All deaths and causes of death were identified from the Chinese National Center for Disease Control and Prevention (CDC) Surveillance Points System. Analyses were constructed by multiple Cox regression models. The study comprised a total of 41,911 cancer patients, of which 9,050 events were observed. Higher serum creatinine fluctuation was associated with an elevated risk of one-year mortality significantly, with a hazard ratio of 1.62 (95% confidence interval, 1.52-1.72; P <0.001) for the standard deviation of creatinine in quartile four compared with quartile one. Furthermore, the association persisted even though all creatinine was within the clinically normal range. The coefficient of variation of creatinine showed similar results. Higher serum creatinine fluctuation during hospital admission is associated with an elevated risk of one-year mortality among cancer patients, even if the fluctuation is within the clinically normal range. Cancer Creatinine Mortality Variability Figures Figure 1 Statement of Significance Comprehensive analysis of association between creatinine fluctuation during hospital admission and mortality sheds new light on the development of new cancer prognostic tools that take the variability of creatinine into account. Introduction Cancer is a leading cause of death worldwide, accounting for an estimated nearly 10 million deaths in 2020. 1 The burden of cancer is expected to continue to increase due to factors such as population growth, aging, and unhealthy lifestyles. Creatinine is a waste product produced by muscle metabolism and removed by the kidneys from blood and excreted in the urine. Its variability is the key indicator used to evaluate renal function and has a close and reciprocal relationship with the development and progression of human cancers. 2–5 An abrupt change in serum creatinine is always regarded as the gold standard for phenotyping acute kidney injury (AKI) which has multifaceted nature, leading to the potential acute changes in the individuals' glomerular filtration rate that is correlated with morbidity. 6–8 Even small variability of creatinine in outpatients with chronic kidney disease (CKD) or in inpatients is also significantly associated with these patients' higher risk of long-term mortality. 5,9–11 Furthermore, serum creatinine is primarily influenced by skeletal muscle mass, as it is released from muscle tissue. Muscle wasting is a prevalent occurrence in patients with cancer, 12 leading to higher chances of chemotherapy side effects, post-surgery complications, and reduced physical function, quality of life, and survival rates. 13 Additionally, anticancer therapies, such as chemotherapy and radiation therapy, can be highly toxic to the kidneys and contribute to the development of acute kidney injury. 14–16 Conversely, kidney injury complicates malignancy and its treatment, 15,17 such as electrolyte imbalances and fluid accumulation, which can be life-threatening in critically ill patients. 18,19 Creatine metabolism is also closely associated with the nutrient metabolism and growth of various tumors. 20,21 Cancer cells require additional energy to sustain their rapid proliferation through metabolic adaptations. 22 After being synthesized in the liver, creatine is transported to the muscles, where it is converted into its phosphorylated form, phosphocreatine, which serves as an energy reserve for muscle contraction 23 and may also play a comparable role in malignant cells. 24,25 Above all, the rationale for the association between serum creatinine and cancer is theoretically straightforward. Although a few studies demonstrated that the creatinine-cystatin C ratio is associated with survival opportunities in cancer patients, the consequences of creatinine variability are poorly understood. 2,26 Insight regarding the magnitude of creatinine fluctuations plays an increasingly important role instead of the baseline value to prevent physicians from erroneously interpreting while neither creatinine level nor its variability is considered for inclusion in most of the existing cancer prognostic models. 27,28 One-year mortality can provide valuable information on the initial response to treatment and the early survival outcomes of cancer patients. It can help identify patients who may require additional interventions or closer monitoring. Therefore, to fill the gap in creatinine variability research among vulnerable cancer patients, the primary objective of this study is to examine the association between blood creatinine variability and the risk of one-year mortality after hospital admission among patients with solid tumors. Methods Data Sources and Study Sample We conducted a multi-center study using data from two sources. The basic characteristics and biomarkers of all participants were retrieved from the electronic medical records of eight hospitals in China. Information regarding the death event within one year from admission including the date and cause of death was identified from the Chinese National Center for Disease Control and Prevention (CDC) Surveillance Points System. The Ethics Committee of Tsinghua University approved this study (NO. 20230008) and abided by the Declaration of Helsinki principles. For this study, a total of 121,649 primary admission non-maternal patients with solid tumors aged 18 years or older were identified between January 1, 2013, and December 31, 2019. Patients who were admitted only for physical examination were excluded. For the analysis, we narrowed down the subjects to those with three or more creatinine measurements. Outliers were identified by setting values below the 1st percentile or above the 99th percentile. Ultimately, a total of 41,911 subjects were included in the analysis. Exposure and Outcome Variables Blood creatinine was measured by trained staff of the laboratory department in the hospitals. The variability of blood creatinine was evaluated by the standard deviation (SD) and coefficient of variation (CV) across multiple measurements for each individual. SD was calculated using a standard formula. The CV was computed as the SD divided by the mean of the data. Time to event was defined as the days of stay between admission and the date of death or censoring (365 days). Covariates including sex, age, and cancer type (liver cancer, lung cancer, colorectal cancer, gastric cancer, pancreatic cancer, renal cancer, esophageal cancer, and others), Charlson Elixhauser Index (CEI, ≤ 0, 1–4, 5–8, ≥ 9), days of hospitalization, renal injury (yes/no), chemoradiotherapy (radiotherapy, chemotherapy, immunotherapy, and other), operation (none, general anesthesia, local anesthesia), systolic blood pressure (mmHg), respiratory rate (per minute), pulse (per minute), and other biomarkers (including alpha-fetoprotein, albumin, total bilirubin, white blood cell counts, glucose, total- and LDL-cholesterol) as categorical variables (low, normal, high). We used the International Classification of Diseases Tenth Revision (ICD-10) to identify our conditions of interest, including cancer, renal injury, and other comorbidities. The related ICD-10 code was displayed in eTable 1. Statistical Analysis Demographic and clinical characteristics of patients with solid tumors during the first hospitalization were compared across the survival outcome. Continuous variables were compared using analysis of variance and categorical variables with the χ2-test. Missing values of covariates were multiply-imputed using the “chained equations” method, assuming missing at random with a monotone missing mechanism, and used five imputed data sets. Multiple Cox regression models were then constructed to estimate the association between variability of creatinine (i.e., SD and CV of creatinine) and mortality outcome within one year, after accounting for potential confounders. Model 1 included sex, age, and cancer type; Model 2 was adjusted for Model 1 plus CEI, days of hospitalization, renal injury, chemoradiotherapy, operation, systolic blood pressure, respiratory rate, and pulse; Model 3 was further adjusted for the first value of creatinine, alpha-fetoprotein, albumin, total bilirubin, white blood cell counts, glucose, total- and LDL-cholesterol on the bases of Model 2. We also constructed models that only included the first or mean value of creatinine instead of the creatinine fluctuation for the comparison of the two protocols. Furthermore, a restricted cubic spline plot for the variability of creatinine and risk of deceased within a year from admission. Fitted curves were used to aid in visually assessing statistical trends. For the multiple measurements of creatinine, whether variability within the clinically normal range is a key confounder. Thus, we separated the subjects into two groups according to whether they had at least one value of creatinine out of the clinically normal range, and this was used to conduct the subgroup analysis. Furthermore, the time interval between consecutive measurements was inconsistent, which may induce additional bias. In order to control the time interval between two adjacent measurements, we conducted a sensitivity analysis using the first creatinine value in every 3-day period test during that hospital stay. In both above cases, the cohort was re-stratified using the new quartile of serum creatinine. Besides, subgroup analysis stratified by absolute changes of creatinine from the first to the last measurement ( 5 µmol/L), tumor types, sex, age, measurement times of creatinine, and whether had undergone chemoradiotherapy or surgery were also performed. In order to assess potential biases induced by imputation data, we carried out a post hoc sensitivity analysis after excluding those participants with imputation data. Clinically, cancer universally involves renal, 14–16 thus sensitivity analysis excluding those with renal failure was also conducted. Finally, they were more likely due to death or not the primary admission if hospitalization was fewer than 10 days. Therefore, we also excluded those patients for sensitivity analysis. All statistical analyses were performed with STATA (Stata Release 17.0; Stata Corporation, College Station, TX). And plots were drawn using the SAS Version 9.4 software (SAS Institute). Results The study included a total of 41,911 cancer patients, with an average age of 57.99 (± 11.91) years and a 69.8% male representation. The distribution of tumor types of the participants was detailed in eTable 2 . In this cohort, the most common cancers were liver cancer [18,110 (43.2%)], followed by lung cancer [5,185 (12.4%)], and colorectal cancer [4,113 (9.8%)]). A total of 9,050 death events were observed within one year since admission and the mean days from admission to death were 151.24 (± 103.02) days. Table 1 presents the baseline characteristics and outcomes of the patients after one year from admission. Compared with patients who died within one year since admission, the surviving patients exhibited lower SD and CV of serum creatinine but higher mean and first creatinine (all P < 0.001); they were also younger ( P < 0.001), more likely to be male ( P < 0.001), and had higher respiratory rates, pulses, ECI, and a higher proportion of chemoradiotherapy, and renal injury (all P < 0.001). Table 1 Basic characteristics of participants. Variables Total (n = 41,911) Death (n = 9,050) Alive (n = 32,861) P-Value SD of Cr. 6.39 (4.21–9.46) 7.00 (4.56–11.11) 6.23 (4.15–9.10) < 0.001 CV of Cr. 0.095 (0.064–0.137) 0.107 (0.071–0.160) 0.092 (0.062–0.132) < 0.001 Mean Cr. 69.25 (58.73–80.67) 68.25 (57.25–81.83) 69.46 (59.18–80.42) < 0.001 First Cr. 71.30 (61.00–83.00) 70.30 (58.80–83.00) 71.90 (61.10–82.70) < 0.001 Low 3,852 (9.2%) 1,347 (14.9%) 2,505 (7.6%) < 0.001 Normal 36,247 (86.5%) 7,045 (77.8%) 29,202 (88.9%) High 1,812 (4.3%) 658 (7.3%) 1,154 (3.5%) Number of Cr. measurement 4 ( 3 – 6 ) 4 ( 3 – 7 ) 4 ( 3 – 6 ) < 0.001 Mean interval days 3.83 (2.67–5.33) 4.00 (2.75–5.25) 3.80 (2.67–5.33) 0.75 Inpatient days 17 ( 12 – 23 ) 18 ( 13 – 25 ) 17 ( 12 – 23 ) < 0.001 Sex < 0.001 Female 12,671 (30.2%) 2,344 (25.9%) 10,327 (31.4%) Male 29,240 (69.8%) 6,706 (74.1%) 22,534 (68.6%) Age 57.99 ± 11.91 59.48 ± 12.72 57.58 ± 11.64 < 0.001 DBP 80 (74–84) 80 (74–86) 80 (74–84) 0.60 SBP 126 (120–136) 126 (118–136) 126 (120–136) < 0.001 Respiratory rate 18 ( 18 – 20 ) 19 ( 18 – 20 ) 18 ( 18 – 20 ) < 0.001 Pulses 78 (74–84) 80 (76–88) 78 (73–83) < 0.001 Chemoradiotherapy < 0.001 None 38,212 (91.2%) 7,759 (85.7%) 30,453 (92.7%) Radiotherapy 602 (1.4%) 192 (2.1%) 410 (1.2%) Chemotherapy 2,019 (4.8%) 654 (7.2%) 1,365 (4.2%) Other 1,078 (2.6%) 445 (4.9%) 633 (1.9%) Operation < 0.001 None 13,173 (31.4%) 4,277 (47.3%) 8,896 (27.1%) General anesthesia 17,655 (42.1%) 1,502 (16.6%) 16,153 (49.2%) Local anesthesia 11,083 (26.4%) 3,271 (36.1%) 7,812 (23.8%) Renal injury < 0.001 No 40,976 (97.8%) 8,772 (96.9%) 32,204 (98.0%) Yes 935 (2.2%) 278 (3.1%) 657 (2.0%) Comorbidity index < 0.001 ≤ 0 13,756 (32.8%) 1,311 (14.5%) 12,445 (37.9%) 1–4 13,096 (31.2%) 2,355 (26.0%) 10,741 (32.7%) 5–8 1,605 (3.8%) 344 (3.8%) 1,261 (3.8%) ≥ 9 13,454 (32.1%) 5,040 (55.7%) 8,414 (25.6%) Data were expressed as means ± standard deviation; Continuous variables were expressed as median [percentiles 25th-75th], unless otherwise stated; categorical variables were expressed as absolute numbers and percentages. Abbreviations: Cr., creatinine; CV, coefficient of variation (standard deviation/mean); SD, standard deviation; In addition, the percent of elevated biomarkers like alpha-fetoprotein, total bilirubin, LDL-cholesterol, total cholesterol, glucose, and white blood cell counts were significantly higher ( P < 0.001), while albumin and HDL-cholesterol were significantly lower ( P < 0.001) in patients who died within one year ( eTable 3 ). We showed the association between serum creatinine fluctuation and one-year mortality in Table 2 . We found higher serum creatinine fluctuation to be significantly associated with an elevated risk of one-year mortality – compared with quartile one of SD , those with quartile four had an HR of 1.62 (95% CI, 1.52–1.72; P < 0.001) in the fully adjusted model. CV of creatinine showed similar results. Figure 1 showed the non-linear association between the variability of creatinine and the risk of the outcome. An increase in creatinine variability during hospital admission was associated with a higher risk of one-year mortality. Table 2 Cox proportional hazards regression analyses of the association between creatinine variability and the risk of deceased within a year from discharge. Variables Case (%) HR (95% CI) Model 1 Model 2 Model 3 SD of Cr. Quartile 1 1,964 (18.75%) 1.00 ( Ref. ) 1.00 ( Ref. ) 1.00 ( Ref. ) Quartile 2 2,019 (19.27%) 1.03 (0.97–1.10) 1.12 (1.05–1.19) 1.10 (1.03–1.17) Quartile 3 2,134 (20.37%) 1.11 (1.04–1.18) 1.23 (1.16–1.31) 1.21 (1.14–1.29) Quartile 4 2,933 (27.99%) 1.72 (1.62–1.82) 1.75 (1.65–1.85) 1.62 (1.52–1.72) p-trend < 0.001 < 0.001 < 0.001 CV of Cr. Quartile 1 1,830 (17.47%) 1.00 ( Ref. ) 1.00 ( Ref. ) 1.00 ( Ref. ) Quartile 2 1,975 (18.85%) 1.11 (1.04–1.18) 1.18 (1.11–1.26) 1.16 (1.09–1.24) Quartile 3 2,191 (20.91%) 1.28 (1.20–1.36) 1.38 (1.30–1.47) 1.33 (1.25–1.42) Quartile 4 3,054 (29.15%) 2.02 (1.91–2.14) 1.99 (1.87–2.11) 1.77 (1.67–1.88) p-trend < 0.001 < 0.001 < 0.001 Model 1 was adjusted for sex, age, cancer type; Model 2 was further adjusted for comorbidity index (≤ 0, 1–4, 5–8, ≥ 9), days of hospitalization, renal injury (yes/no), chemoradiotherapy (radiotherapy, chemotherapy, immunotherapy, and other), operation (none, general anesthesia, local anesthesia), systolic blood pressure (mmHg), respiratory rate (per minute), pulse (per minute); Model 3 was adjusted for the first value of Cr., other biomarkers (including alpha-fetoprotein, albumin, total bilirubin, white blood cell counts, glucose, total- and LDL-cholesterol) as categorical variables (low, normal, high) on the bases of model 2. Abbreviations: CI, confidence interval; Cr., creatinine; CV, coefficient of variation (standard deviation/mean); HR, hazard ratio; SD, standard deviation; Furthermore, the variability of creatinine within the clinically normal range was also related to the outcome event, with HR 1.80 (95% CI, 1.67–1.93) of SD and 1.80 (95% CI, 1.68–1.93) of CV (quartile four vs. quartile one, Table 3 ). Having one or more values of creatinine out of the clinically normal range reduced the association between creatinine variability and one-year mortality, but the association remained statistically significant ( P < 0.001). Our findings remain valid after controlling for the measurement interval of serum creatinine for a 3-days period (Table 4 ). In this sensitivity analysis, the HR of SD and CV were 1.57 (95% CI, 1.44–1.71) and 1.73 (95% CI, 1.58–1.88) in quartile four compared with quartile one, respectively. Table 3 Stratified analysis according to whether has value out of the clinically normal range and the number of creatinine measurements. In the normal range With value out of normal range Case (%) HR (95% CI) Case (%) HR (95% CI) SD of Cr. Quartile 1 1,490 (16.44%) 1.00 ( Ref. ) 457 (32.27%) 1.00 ( Ref. ) Quartile 2 1,590 (17.55%) 1.15 (1.07–1.23) 456 (32.43%) 1.06 (0.93–1.21) Quartile 3 1,718 (18.96%) 1.32 (1.23–1.41) 469 (32.89%) 1.16 (1.01–1.32) Quartile 4 2,247 (24.79%) 1.80 (1.67–1.93) 623 (44.00%) 1.48 (1.28–1.72) p-trend < 0.001 < 0.001 CV of Cr. Quartile 1 1,448 (15.98%) 1.00 ( Ref. ) 417 (29.45%) 1.00 ( Ref. ) Quartile 2 1,550 (17.10%) 1.16 (1.08–1.24) 448 (31.62%) 1.12 (0.98–1.28) Quartile 3 1,729 (19.08%) 1.36 (1.26–1.45) 508 (35.90%) 1.28 (1.12–1.46) Quartile 4 2,318 (25.58%) 1.80 (1.68–1.93) 632 (44.63%) 1.49 (1.31–1.71) p-trend < 0.001 < 0.001 Adjusted for Model 3; Abbreviations: CI, confidence interval; Cr., creatinine; CV, coefficient of variation (standard deviation/mean); HR, hazard ratio; SD, standard deviation; Table 4 Sensitivity analysis using the first creatinine results in every 3-day period. Variables Case (%) HR (95% CI) Model 1 Model 2 Model 3 SD of Cr. Quartile 1 996 (19.18) 1.00 ( Ref. ) 1.00 ( Ref. ) 1.00 ( Ref. ) Quartile 2 1,002 (19.29) 1.01 (0.93–1.10) 1.08 (0.99–1.18) 1.06 (0.97–1.16) Quartile 3 1,003 (19.31) 1.03 (0.94–1.13) 1.14 (1.04–1.24) 1.13 (1.03–1.23) Quartile 4 1,593 (30.67) 1.80 (1.66–1.95) 1.69 (1.56–1.83) 1.57 (1.44–1.71) p-trend < 0.001 < 0.001 < 0.001 CV of Cr. Quartile 1 896 (17.25) 1.00 ( Ref. ) 1.00 ( Ref. ) 1.00 ( Ref. ) Quartile 2 968 (18.64) 1.11 (1.01–1.21) 1.18 (1.08–1.29) 1.14 (1.04–1.25) Quartile 3 1,093 (21.04) 1.28 (1.17–1.40) 1.36 (1.25–1.49) 1.32 (1.20–1.44) Quartile 4 1,637 (31.52) 2.14 (1.97–2.33) 1.95 (1.79–2.13) 1.73 (1.58–1.88) p-trend < 0.001 < 0.001 < 0.001 Abbreviations: CI, confidence interval; Cr., creatinine; CV, coefficient of variation (standard deviation/mean); HR, hazard ratio; SD, standard deviation; In addition, stratified analyses according to tumor types were shown in eFigure 1 . Consistent with the main results, a positive association between creatinine variability and risk of one-year mortality was found among all but patients with renal carcinoma and esophageal cancer. Subgroup analyses by absolute changes of creatinine from the first to the last measurement were shown in eTable 4 ; and sex, age, measurement times of creatinine, and whether had undergone chemoradiotherapy or surgery ( eFigures 2 − 1 and 2–2 ); and sensitivity analyses ( eFigures 3 − 1 and 3 − 2 ) after excluding those with imputation data, renal failure, and hospitalization less than 10 days did not significantly change the conclusion. Finally, we also investigated the association between the mean and first creatinine test and mortality within one year. The results were shown in eTables 5 and 6, and eFigures 4 − 1, 4 − 2, 5 − 1, and 5 − 2. These results were completely distinct from the variability of creatinine, that is a U-shape association between the mean/first creatinine and the risk of one-year mortality. Discussion The present study enrolled a large sample of 41,911 patients with diverse cancer and demonstrated that higher serum creatinine fluctuation during hospital admission was associated with a higher mortality risk within one year. Notably, fluctuation of creatinine was associated with mortality regardless of whether all the values were within the clinically normal range or not. Furthermore, stratified analyses according to sex, age, measurement times of creatinine, and whether had undergone chemoradiotherapy or surgery did not significantly change the results. Sensitivity analyses also confirm the robustness of the conclusion. In previous studies, researchers evaluated the predictive value of absolute or relative changes in creatinine on mortality and illustrated that changes in serum creatinine identified the patients who were at increased risk for short- and long-term death. 29–36 Our findings extend and clarify those of previous studies and extrapolated the conclusion to patients with cancer. Moreover, some existing observations reached conflicting conclusions. For example, they found that cancer patients are at higher risk of death with increases in serum creatinine or AKI. 35,37–40 In contrast, an increase in serum creatinine was not associated with hematological adverse events in patients with breast cancer. 36 In addition, one study based on the UK biobank illustrated that creatinine-cystatin C was associated with neither cancer incidence nor the risk of cancer death. 3 Previous findings aimed to elucidate the influence of the baseline value or absolute change of serum creatinine but not the overall variability of it on mortality for patients with cancer. However, the fluctuation may present a renal adaptability metric, a concept not measured by one value or absolute change. Our findings compensated for this gap and highlight the usefulness of serum creatinine fluctuation in at-risk cancer patients, allowing for risk stratification of those individuals to lower cancer mortality. Cancer prognosis is of keen interest to cancer patients, their caregivers, and providers owing to the high mortality rates. 41,42 An increasing body of research has focused on developing prognostic tools to guide patient-physician communication and decision-making. Until now, more than thirty interactive cancer prognostic tools have been developed, which incorporate cancer, demographic, and genetic characteristics, co-morbidities and therapy information, and modifiable risk factors. 27,28 However, these prognostic tools largely fail to account for biomarker changes over time, such as the variability of creatinine. Our study sheds new light on the development of new cancer prognostic tools which take the variability of biomarkers into account. Our study showed robust associations between creatinine fluctuation and poor mortality outcomes in almost all subgroups. However, what is unexpected is the negative association among patients with renal carcinoma. A potential explanation may be the small sample size, especially since the case numbers were rather low, which vary in size from seven to dozens in each group. Confounding factors including drug therapies and treatment to prevent kidney disease progression are other possible reasons. This suggests that further research is needed to fully understand the relationship between serum creatinine variability and one-year mortality in renal carcinoma patients. The present study was not designed to explore the mechanism, but several potential explanations may explain these results. First, the variability of serum creatinine is usually ascribed to changes in GFR and reflects the capacity to adapt to nephrotoxic insults. 43 Elevated change of GFR means poor ability in maintaining renal homeostasis and is independently associated with all-cause mortality. 10,43 Second, creatinine generation rate is another important indicator for the variability of serum creatinine. Both the loss of muscle mass and poor nutrition can induce the change in creatinine generation rate, 44 and they are also strong risk factors for cancer mortality. 45 Finally, similar to AKI, a larger creatinine fluctuation frequently occurs in patients with tumors caused by chemotherapy-associated nephrotoxicity, 46 which always indicates an advanced stage of cancer. Despite the mechanisms under the association between creatinine fluctuation and mortality are not well understood, we believe that this finding has important clinical implications for the identification of high-risk cancer patients. This study has some limitations. First, we did not include any information about drug therapy during hospitalization. These pharmacologic strategies hold great promise to impact the variability of creatinine during hospitalization. However, given that all participants were patients with cancer, we performed the subgroup analysis based on cancer type, which accounted for some degrees of the confounding of treatment. Second, limited by the availability and accessibility of relevant data, the primary outcome was only one-year mortality without a longer follow-up. But one-year mortality is a commonly used endpoint in cancer research and is often considered clinically meaningful, for example, identifying patients who may require closer monitoring during hospitalization. Third, we did not define AKI based on the KDIGO AKI definition given the heterogeneity and high missing rate of urine output. A previous study conducted by Bhatraju1 et.al demonstrated that patients with different AKI sub-phenotypes defined by creatinine trajectory had different risks for mortality. 32 Therefore, we performed a subgroup analysis according to the degree of creatinine variability and renal failure defined by the ICD code to address the effect of kidney status. Fourth, patients with more advanced cancer are associated with a longer hospital stay and larger measurement times of creatinine, which could induce larger creatinine fluctuation. So, the potential effects of reverse causality cannot be excluded. Fifth, it is not possible to impute causality or provide a deeper mechanistic investigation due to the observational study design of this study. Instead, our findings provided exciting novel clues awaiting now in-depth analysis in follow-up studies. Finally, only Chinese patients were involved, which may limit the generalizability of these findings to other populations. Despite these limitations, the results of this study have the potential to inform clinical practice by providing important information on the role of creatinine variability in the care of cancer patients. Given this finding, it is important for healthcare providers to monitor creatinine levels closely in patients with malignancy to identify fluctuations and intervene early to mitigate the risk of death. Further, new strategies considering the variability of creatinine for monitoring and mitigating the risk of adverse outcomes for cancer patients could be developed. Collectively, the relationship between blood creatinine fluctuations and the risk of death in patients with malignancy is complex and requires ongoing research and monitoring. In aggregate, the study highlights the importance of monitoring and controlling serum creatinine levels in cancer patients during hospital admission to reduce the risk of mortality. And provides important information on developing prevention strategies and triage decisions, and improves the survival of patients with cancer. Above all, our observations have important clinical implications and call for further research in this area. Declarations Acknowledgments: This research has been done using the electronic medical records of eight hospitals in China and linked with the Chinese National Center for Disease Control and Prevention (CDC) Surveillance Points System. The authors thank all of the participants and staff of the eight hospitals and China CDC. Author contributions: Lin Li and Zhihui Li designed the work; Lin Li contributed to the acquisition of the data set; Lin Li, Huanhuan Yang, and Yi Zhang analyzed the data, completed the tables and figures, and are in charge of the interpretation of data; Huanhuan Yang drafted the manuscript; all other authors reviewed the work critically for important intellectual content; and all authors finally approved the version to be published. Financial Support: This work was supported by the National Natural Science Foundation of China (grant number 72274211), and the Research Fund, Vanke School of Public Health, Tsinghua University (grant number 2022BH012). Authors’ Disclosures: The authors have no conflicts of interest to report. References Ferlay JEM, Lam F, Colombet M, Mery L, Piñeros M et al. Global Cancer Observatory: Cancer Today. Lyon: International Agency for Research on Cancer; 2020 ( https://gco.iarc.fr/today , accessed February 2021). Jung CY, Kim HW, Han SH, et al. Creatinine-cystatin C ratio and mortality in cancer patients: a retrospective cohort study. J Cachexia Sarcopenia Muscle Aug. 2022;13(4):2064–72. 10.1002/jcsm.13006 . Lees JS, Ho F, Parra-Soto S, et al. Kidney function and cancer risk: An analysis using creatinine and cystatin C in a cohort study. EClinicalMedicine Aug. 2021;38:101030. 10.1016/j.eclinm.2021.101030 . Levin ASP, Bilous RW, Coresh J, De Francisco ALM, De Jong PE, et al. Kidney disease: Improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Supplements. 2013;3(1):1–150. 10.1038/kisup.2012.73 . Perkins RM, Tang X, Bengier AC, Kirchner HL, Bucaloiu ID. Variability in estimated glomerular filtration rate is an independent risk factor for death among patients with stage 3 chronic kidney disease. Kidney Int Dec. 2012;82(12):1332–8. 10.1038/ki.2012.281 . Coca SG, Singanamala S, Parikh CR. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int Mar. 2012;81(5):442–8. 10.1038/ki.2011.379 . Bellomo R, Kellum JA, Ronco C. Acute kidney injury. Lancet Aug 25. 2012;380(9843):756–66. 10.1016/s0140-6736(11)61454-2 . Siew ED, Ware LB, Ikizler TA. Biological markers of acute kidney injury. J Am Soc Nephrol. May 2011;22(5):810–20. 10.1681/asn.2010080796 . Coca SG, Peixoto AJ, Garg AX, Krumholz HM, Parikh CR. The prognostic importance of a small acute decrement in kidney function in hospitalized patients: a systematic review and meta-analysis. Am J Kidney Dis Nov. 2007;50(5):712–20. 10.1053/j.ajkd.2007.07.018 . Turin TC, Coresh J, Tonelli M, et al. Change in the estimated glomerular filtration rate over time and risk of all-cause mortality. Kidney Int Apr. 2013;83(4):684–91. 10.1038/ki.2012.443 . Kao SS, Kim SW, Horwood CM, et al. Variability in inpatient serum creatinine: its impact upon short- and long-term mortality. Qjm Oct. 2015;108(10):781–7. 10.1093/qjmed/hcv020 . Schmidt SF, Rohm M, Herzig S, Berriel Diaz M. Cancer Cachexia: More Than Skeletal Muscle Wasting. Trends Cancer Dec. 2018;4(12):849–60. 10.1016/j.trecan.2018.10.001 . Prado CM, Lieffers JR, McCargar LJ, et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol Jul. 2008;9(7):629–35. 10.1016/s1470-2045(08)70153-0 . Rosner MH, Perazella MA. Acute kidney injury in the patient with cancer. Kidney Res Clin Pract Sep. 2019;30(3):295–308. 10.23876/j.krcp.19.042 . Humphreys BD, Soiffer RJ, Magee CC. Renal Failure Associated with Cancer and Its Treatment: An Update. J Am Soc Nephrol. 2005;16(1). Kapoor M, Chan GZ. Jul. Malignancy and renal disease. Crit Care Clin . 2001;17(3):571 – 98, viii. 10.1016/s0749-0704(05)70199-8 . Rosolem MM, Rabello LS, Lisboa T, et al. Critically ill patients with cancer and sepsis: clinical course and prognostic factors. J Crit Care Jun. 2012;27(3):301–7. 10.1016/j.jcrc.2011.06.014 . Lameire N, Vanholder R, Van Biesen W, Benoit D. Acute kidney injury in critically ill cancer patients: an update. Crit Care Aug 2. 2016;20(1):209. 10.1186/s13054-016-1382-6 . Rosner MH, Perazella MA. Acute Kidney Injury in Patients with Cancer. N Engl J Med. May 2017;4(18):1770–81. 10.1056/NEJMra1613984 . Zhang L, Bu P. The two sides of creatine in cancer. Trends Cell Biol May. 2022;32(5):380–90. 10.1016/j.tcb.2021.11.004 . Kazak L, Cohen P. Creatine metabolism: energy homeostasis, immunity and cancer biology. Nat Rev Endocrinol Aug. 2020;16(8):421–36. 10.1038/s41574-020-0365-5 . Vander Heiden MG, DeBerardinis RJ. Understanding the Intersections between Metabolism and Cancer Biology. Cell . Feb 9. 2017;168(4):657–669. 10.1016/j.cell.2016.12.039 . Cooper R, Naclerio F, Allgrove J, Jimenez A. Creatine supplementation with specific view to exercise/sports performance: an update. J Int Soc Sports Nutr Jul. 2012;20(1):33. 10.1186/1550-2783-9-33 . Kurmi K, Hitosugi S, Yu J, et al. Tyrosine Phosphorylation of Mitochondrial Creatine Kinase 1 Enhances a Druggable Tumor Energy Shuttle Pathway. Cell Metab Dec. 2018;4(6):833–e8478. 10.1016/j.cmet.2018.08.008 . Fenouille N, Bassil CF, Ben-Sahra I, et al. The creatine kinase pathway is a metabolic vulnerability in EVI1-positive acute myeloid leukemia. Nat Med. Mar 2017;23(3):301–13. 10.1038/nm.4283 . Zheng C, Wang E, Li JS, et al. Serum creatinine/cystatin C ratio as a screening tool for sarcopenia and prognostic indicator for patients with esophageal cancer. BMC Geriatr Mar. 2022;15(1):207. 10.1186/s12877-022-02925-8 . Rabin BA, Gaglio B, Sanders T, et al. Predicting cancer prognosis using interactive online tools: a systematic review and implications for cancer care providers. Cancer Epidemiol Biomarkers Prev Oct. 2013;22(10):1645–56. 10.1158/1055-9965.Epi-13-0513 . Seow H, Tanuseputro P, Barbera L, et al. Development and Validation of a Prognostic Survival Model With Patient-Reported Outcomes for Patients With Cancer. JAMA Netw Open Apr. 2020;1(4):e201768. 10.1001/jamanetworkopen.2020.1768 . Coca SG, Zabetian A, Ferket BS, et al. Evaluation of Short-Term Changes in Serum Creatinine Level as a Meaningful End Point in Randomized Clinical Trials. J Am Soc Nephrol Aug. 2016;27(8):2529–42. 10.1681/asn.2015060642 . Kao SS, Kim SW, Horwood CM, et al. Variability in inpatient serum creatinine: its impact upon short- and long-term mortality. QJM: Int J Med. 2015;108(10):781–7. 10.1093/qjmed/hcv020 . Korenkevych D, Ozrazgat-Baslanti T, Thottakkara P, et al. The Pattern of Longitudinal Change in Serum Creatinine and 90-Day Mortality After Major Surgery. Ann Surg Jun. 2016;263(6):1219–27. 10.1097/sla.0000000000001362 . Bhatraju PK, Mukherjee P, Robinson-Cohen C, et al. Acute kidney injury subphenotypes based on creatinine trajectory identifies patients at increased risk of death. Crit Care Nov. 2016;17(1):372. 10.1186/s13054-016-1546-4 . Garlo KG, Bates DW, Seger DL, Fiskio JM, Charytan DM. Association of Changes in Creatinine and Potassium Levels After Initiation of Renin Angiotensin Aldosterone System Inhibitors With Emergency Department Visits, Hospitalizations, and Mortality in Individuals With Chronic Kidney Disease. JAMA Netw Open Nov. 2018;2(7):e183874. 10.1001/jamanetworkopen.2018.3874 . Cullaro G, Hsu CY, Lai JC. Variability in serum creatinine is associated with waitlist and post-liver transplant mortality in patients with cirrhosis. Hepatol Oct. 2022;76(4):1069–78. 10.1002/hep.32497 . Samuels J, Ng CS, Nates J, et al. Small increases in serum creatinine are associated with prolonged ICU stay and increased hospital mortality in critically ill patients with cancer. Support Care Cancer Oct. 2011;19(10):1527–32. 10.1007/s00520-010-0978-7 . Maeda A, Irie K, Hashimoto N, et al. Serum concentration of the CKD4/6 inhibitor abemaciclib, but not of creatinine, strongly predicts hematological adverse events in patients with breast cancer: a preliminary report. Invest New Drugs Feb. 2021;39(1):272–7. 10.1007/s10637-020-00994-3 . Córdova-Sánchez BM, Herrera-Gómez Á, Ñamendys-Silva SA. Acute Kidney Injury Classified by Serum Creatinine and Urine Output in Critically Ill Cancer Patients. Biomed Res Int. 2016;2016:6805169. 10.1155/2016/6805169 . Seylanova N, Crichton S, Zhang J, Fisher R, Ostermann M. Acute kidney injury in critically ill cancer patients is associated with mortality: A retrospective analysis. PLoS ONE. 2020;15(5):e0232370. 10.1371/journal.pone.0232370 . Kang E, Park M, Park PG, et al. Acute kidney injury predicts all-cause mortality in patients with cancer. Cancer Med Jun. 2019;8(6):2740–50. 10.1002/cam4.2140 . Slagelse C, Gammelager H, Iversen LH, Sørensen HT, Christiansen CF. Acute kidney injury and 1-year mortality after colorectal cancer surgery: a population-based cohort study. BMJ Open Mar. 2019;13(3):e024817. 10.1136/bmjopen-2018-024817 . de Bock GH, Bonnema J, Zwaan RE, et al. Patient's needs and preferences in routine follow-up after treatment for breast cancer. Br J Cancer Mar. 2004;22(6):1144–50. 10.1038/sj.bjc.6601655 . Rutten LJ, Arora NK, Bakos AD, Aziz N, Rowland J. Information needs and sources of information among cancer patients: a systematic review of research (1980–2003). Patient Educ Couns Jun. 2005;57(3):250–61. 10.1016/j.pec.2004.06.006 . Al-Aly Z, Balasubramanian S, McDonald JR, Scherrer JF, O'Hare AM. Greater variability in kidney function is associated with an increased risk of death. Kidney Int Dec. 2012;82(11):1208–14. 10.1038/ki.2012.276 . Kovesdy CP, George SM, Anderson JE, Kalantar-Zadeh K. Outcome predictability of biomarkers of protein-energy wasting and inflammation in moderate and advanced chronic kidney disease. Am J Clin Nutr Aug. 2009;90(2):407–14. 10.3945/ajcn.2008.27390 . Wilson FP, Sheehan JM, Mariani LH, Berns JS. Creatinine generation is reduced in patients requiring continuous venovenous hemodialysis and independently predicts mortality. Nephrol Dial Transpl Nov. 2012;27(11):4088–94. 10.1093/ndt/gfr809 . Kitchlu A, McArthur E, Amir E, et al. Acute Kidney Injury in Patients Receiving Systemic Treatment for Cancer: A Population-Based Cohort Study. J Natl Cancer Inst Jul. 2019;1(7):727–36. 10.1093/jnci/djy167 . Additional Declarations No competing interests reported. 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Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLCCDxUSPPwSYKaEDFE6GGecsZGTnMHA2ADUwkOUFmbetjRjgxtgLQyEtRgcP2P4mYftcOLm283HH92oseBhYD98dANeLWdyjCXn8BxO3HbnWGJzzjGgw3jS0m7g1XIgx0DijQRQy40cw+YcNqAWCR4z/FrOvzH+wWMAdNgMkJZ/xGi5kWMmyZMA9L4EUEtuGxFaJG88K7OcccBGTuJGWuLs3D4JHjZCfuE7n7z5xkege/hnJB/4nPOtTo6f/fAxvFoUDnAYoIqw4VMOAvIN7A8IqRkFo2AUjIKRDgBX602g4hB2IgAAAABJRU5ErkJggg==","orcid":"","institution":"Hospital Management Institute, Chinese People’s Liberation Army General Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liu","suffix":""},{"id":326875925,"identity":"86c3f9fd-a18c-4fda-852f-ae1f20254959","order_by":10,"name":"Zhihui Li","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-26 02:05:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4639262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4639262/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60635065,"identity":"8ef0d50a-0105-4b17-a7d5-803f7540bb06","added_by":"auto","created_at":"2024-07-19 01:57:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1049264,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline for creatinine fluctuation and risk of 1-year mortality.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval; Cr., creatinine; CV, coefficient of variation (standard deviation/mean); HR, hazard ratio; SD, standard deviation;\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4639262/v1/aa8b36cb4b228dda9317fae5.jpg"},{"id":75432655,"identity":"e1286789-c194-4c33-943f-be7a2f0e5d18","added_by":"auto","created_at":"2025-02-04 13:47:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1963402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4639262/v1/afb2bf7f-174c-4610-a55c-92772d6d65ae.pdf"},{"id":60635067,"identity":"e16802b8-5a1a-4030-acce-e37ffad84def","added_by":"auto","created_at":"2024-07-19 01:57:43","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4740165,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4639262/v1/e7c9afad0b4be25eb0452a75.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of serum creatinine variability and risk of 1-year mortality among patients with cancer","fulltext":[{"header":"Statement of Significance","content":"\u003cp\u003eComprehensive analysis of association between creatinine fluctuation during hospital admission and mortality sheds new light on the development of new cancer prognostic tools that take the variability of creatinine into account.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCancer is a leading cause of death worldwide, accounting for an estimated nearly 10\u0026nbsp;million deaths in 2020.\u003csup\u003e1\u003c/sup\u003e The burden of cancer is expected to continue to increase due to factors such as population growth, aging, and unhealthy lifestyles. Creatinine is a waste product produced by muscle metabolism and removed by the kidneys from blood and excreted in the urine. Its variability is the key indicator used to evaluate renal function and has a close and reciprocal relationship with the development and progression of human cancers.\u003csup\u003e2\u0026ndash;5\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAn abrupt change in serum creatinine is always regarded as the gold standard for phenotyping acute kidney injury (AKI) which has multifaceted nature, leading to the potential acute changes in the individuals' glomerular filtration rate that is correlated with morbidity.\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e Even small variability of creatinine in outpatients with chronic kidney disease (CKD) or in inpatients is also significantly associated with these patients' higher risk of long-term mortality.\u003csup\u003e5,9\u0026ndash;11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFurthermore, serum creatinine is primarily influenced by skeletal muscle mass, as it is released from muscle tissue. Muscle wasting is a prevalent occurrence in patients with cancer,\u003csup\u003e12\u003c/sup\u003e leading to higher chances of chemotherapy side effects, post-surgery complications, and reduced physical function, quality of life, and survival rates.\u003csup\u003e13\u003c/sup\u003e Additionally, anticancer therapies, such as chemotherapy and radiation therapy, can be highly toxic to the kidneys and contribute to the development of acute kidney injury.\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e Conversely, kidney injury complicates malignancy and its treatment,\u003csup\u003e15,17\u003c/sup\u003e such as electrolyte imbalances and fluid accumulation, which can be life-threatening in critically ill patients.\u003csup\u003e18,19\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCreatine metabolism is also closely associated with the nutrient metabolism and growth of various tumors.\u003csup\u003e20,21\u003c/sup\u003e Cancer cells require additional energy to sustain their rapid proliferation through metabolic adaptations.\u003csup\u003e22\u003c/sup\u003e After being synthesized in the liver, creatine is transported to the muscles, where it is converted into its phosphorylated form, phosphocreatine, which serves as an energy reserve for muscle contraction\u003csup\u003e23\u003c/sup\u003e and may also play a comparable role in malignant cells.\u003csup\u003e24,25\u003c/sup\u003e Above all, the rationale for the association between serum creatinine and cancer is theoretically straightforward.\u003c/p\u003e \u003cp\u003eAlthough a few studies demonstrated that the creatinine-cystatin C ratio is associated with survival opportunities in cancer patients, the consequences of creatinine variability are poorly understood.\u003csup\u003e2,26\u003c/sup\u003e Insight regarding the magnitude of creatinine fluctuations plays an increasingly important role instead of the baseline value to prevent physicians from erroneously interpreting while neither creatinine level nor its variability is considered for inclusion in most of the existing cancer prognostic models.\u003csup\u003e27,28\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOne-year mortality can provide valuable information on the initial response to treatment and the early survival outcomes of cancer patients. It can help identify patients who may require additional interventions or closer monitoring. Therefore, to fill the gap in creatinine variability research among vulnerable cancer patients, the primary objective of this study is to examine the association between blood creatinine variability and the risk of one-year mortality after hospital admission among patients with solid tumors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources and Study Sample\u003c/h2\u003e \u003cp\u003eWe conducted a multi-center study using data from two sources. The basic characteristics and biomarkers of all participants were retrieved from the electronic medical records of eight hospitals in China. Information regarding the death event within one year from admission including the date and cause of death was identified from the Chinese National Center for Disease Control and Prevention (CDC) Surveillance Points System. The Ethics Committee of Tsinghua University approved this study (NO. 20230008) and abided by the Declaration of Helsinki principles.\u003c/p\u003e \u003cp\u003eFor this study, a total of 121,649 primary admission non-maternal patients with solid tumors aged 18 years or older were identified between January 1, 2013, and December 31, 2019. Patients who were admitted only for physical examination were excluded. For the analysis, we narrowed down the subjects to those with three or more creatinine measurements. Outliers were identified by setting values below the 1st percentile or above the 99th percentile. Ultimately, a total of 41,911 subjects were included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExposure and Outcome Variables\u003c/h2\u003e \u003cp\u003eBlood creatinine was measured by trained staff of the laboratory department in the hospitals. The variability of blood creatinine was evaluated by the standard deviation \u003cem\u003e(SD)\u003c/em\u003e and coefficient of variation \u003cem\u003e(CV)\u003c/em\u003e across multiple measurements for each individual. \u003cem\u003eSD\u003c/em\u003e was calculated using a standard formula. The \u003cem\u003eCV\u003c/em\u003e was computed as the \u003cem\u003eSD\u003c/em\u003e divided by the mean of the data.\u003c/p\u003e \u003cp\u003eTime to event was defined as the days of stay between admission and the date of death or censoring (365 days).\u003c/p\u003e \u003cp\u003eCovariates including sex, age, and cancer type (liver cancer, lung cancer, colorectal cancer, gastric cancer, pancreatic cancer, renal cancer, esophageal cancer, and others), Charlson Elixhauser Index (CEI, \u0026le;\u0026thinsp;0, 1\u0026ndash;4, 5\u0026ndash;8, \u0026ge;\u0026thinsp;9), days of hospitalization, renal injury (yes/no), chemoradiotherapy (radiotherapy, chemotherapy, immunotherapy, and other), operation (none, general anesthesia, local anesthesia), systolic blood pressure (mmHg), respiratory rate (per minute), pulse (per minute), and other biomarkers (including alpha-fetoprotein, albumin, total bilirubin, white blood cell counts, glucose, total- and LDL-cholesterol) as categorical variables (low, normal, high). We used the International Classification of Diseases Tenth Revision (ICD-10) to identify our conditions of interest, including cancer, renal injury, and other comorbidities. The related ICD-10 code was displayed in \u003cb\u003eeTable 1.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDemographic and clinical characteristics of patients with solid tumors during the first hospitalization were compared across the survival outcome. Continuous variables were compared using analysis of variance and categorical variables with the χ2-test. Missing values of covariates were multiply-imputed using the \u0026ldquo;chained equations\u0026rdquo; method, assuming missing at random with a monotone missing mechanism, and used five imputed data sets.\u003c/p\u003e \u003cp\u003eMultiple Cox regression models were then constructed to estimate the association between variability of creatinine (i.e., \u003cem\u003eSD\u003c/em\u003e and \u003cem\u003eCV\u003c/em\u003e of creatinine) and mortality outcome within one year, after accounting for potential confounders. Model 1 included sex, age, and cancer type; Model 2 was adjusted for Model 1 plus CEI, days of hospitalization, renal injury, chemoradiotherapy, operation, systolic blood pressure, respiratory rate, and pulse; Model 3 was further adjusted for the first value of creatinine, alpha-fetoprotein, albumin, total bilirubin, white blood cell counts, glucose, total- and LDL-cholesterol on the bases of Model 2. We also constructed models that only included the first or mean value of creatinine instead of the creatinine fluctuation for the comparison of the two protocols. Furthermore, a restricted cubic spline plot for the variability of creatinine and risk of deceased within a year from admission. Fitted curves were used to aid in visually assessing statistical trends.\u003c/p\u003e \u003cp\u003eFor the multiple measurements of creatinine, whether variability within the clinically normal range is a key confounder. Thus, we separated the subjects into two groups according to whether they had at least one value of creatinine out of the clinically normal range, and this was used to conduct the subgroup analysis. Furthermore, the time interval between consecutive measurements was inconsistent, which may induce additional bias. In order to control the time interval between two adjacent measurements, we conducted a sensitivity analysis using the first creatinine value in every 3-day period test during that hospital stay. In both above cases, the cohort was re-stratified using the new quartile of serum creatinine.\u003c/p\u003e \u003cp\u003eBesides, subgroup analysis stratified by absolute changes of creatinine from the first to the last measurement (\u0026lt;\u0026thinsp;5 \u0026micro;mol/L, -5 to 5 \u0026micro;mol/L, \u0026gt;\u0026thinsp;5 \u0026micro;mol/L), tumor types, sex, age, measurement times of creatinine, and whether had undergone chemoradiotherapy or surgery were also performed. In order to assess potential biases induced by imputation data, we carried out a post hoc sensitivity analysis after excluding those participants with imputation data. Clinically, cancer universally involves renal,\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e thus sensitivity analysis excluding those with renal failure was also conducted. Finally, they were more likely due to death or not the primary admission if hospitalization was fewer than 10 days. Therefore, we also excluded those patients for sensitivity analysis.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed with STATA (Stata Release 17.0; Stata Corporation, College Station, TX). And plots were drawn using the SAS Version 9.4 software (SAS Institute).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study included a total of 41,911 cancer patients, with an average age of 57.99 (\u0026plusmn;\u0026thinsp;11.91) years and a 69.8% male representation. The distribution of tumor types of the participants was detailed in \u003cb\u003eeTable 2\u003c/b\u003e. In this cohort, the most common cancers were liver cancer [18,110 (43.2%)], followed by lung cancer [5,185 (12.4%)], and colorectal cancer [4,113 (9.8%)]). A total of 9,050 death events were observed within one year since admission and the mean days from admission to death were 151.24 (\u0026plusmn;\u0026thinsp;103.02) days.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics and outcomes of the patients after one year from admission. Compared with patients who died within one year since admission, the surviving patients exhibited lower \u003cem\u003eSD\u003c/em\u003e and \u003cem\u003eCV\u003c/em\u003e of serum creatinine but higher mean and first creatinine (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); they were also younger (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), more likely to be male (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and had higher respiratory rates, pulses, ECI, and a higher proportion of chemoradiotherapy, and renal injury (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;41,911)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeath (n\u0026thinsp;=\u0026thinsp;9,050)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlive (n\u0026thinsp;=\u0026thinsp;32,861)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-Value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e of Cr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.39 (4.21\u0026ndash;9.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00 (4.56\u0026ndash;11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.23 (4.15\u0026ndash;9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCV\u003c/em\u003e of Cr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095 (0.064\u0026ndash;0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107 (0.071\u0026ndash;0.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092 (0.062\u0026ndash;0.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Cr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.25 (58.73\u0026ndash;80.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.25 (57.25\u0026ndash;81.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.46 (59.18\u0026ndash;80.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst Cr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.30 (61.00\u0026ndash;83.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.30 (58.80\u0026ndash;83.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.90 (61.10\u0026ndash;82.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,852 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,347 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,505 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36,247 (86.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,045 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,202 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,812 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e658 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,154 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Cr. measurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean interval days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.83 (2.67\u0026ndash;5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00 (2.75\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.80 (2.67\u0026ndash;5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,671 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,344 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,327 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29,240 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,706 (74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22,534 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.99\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.48\u0026thinsp;\u0026plusmn;\u0026thinsp;12.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.58\u0026thinsp;\u0026plusmn;\u0026thinsp;11.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (74\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (74\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (74\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (120\u0026ndash;136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (118\u0026ndash;136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (120\u0026ndash;136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (74\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (76\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (73\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemoradiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38,212 (91.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,759 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,453 (92.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e602 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,019 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e654 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,365 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,078 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e633 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,173 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,277 (47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,896 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral anesthesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,655 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,502 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,153 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal anesthesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,083 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,271 (36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,812 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40,976 (97.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,772 (96.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32,204 (98.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e935 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e657 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,756 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,311 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,445 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,096 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,355 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,741 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,605 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,261 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,454 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,040 (55.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,414 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables were expressed as median [percentiles 25th-75th], unless otherwise stated; categorical variables were expressed as absolute numbers and percentages.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: Cr., creatinine; CV, coefficient of variation (standard deviation/mean); SD, standard deviation;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, the percent of elevated biomarkers like alpha-fetoprotein, total bilirubin, LDL-cholesterol, total cholesterol, glucose, and white blood cell counts were significantly higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while albumin and HDL-cholesterol were significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in patients who died within one year (\u003cb\u003eeTable 3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe showed the association between serum creatinine fluctuation and one-year mortality in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We found higher serum creatinine fluctuation to be significantly associated with an elevated risk of one-year mortality \u0026ndash; compared with quartile one of \u003cem\u003eSD\u003c/em\u003e, those with quartile four had an HR of 1.62 (95% CI, 1.52\u0026ndash;1.72; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the fully adjusted model. \u003cem\u003eCV\u003c/em\u003e of creatinine showed similar results. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the non-linear association between the variability of creatinine and the risk of the outcome. An increase in creatinine variability during hospital admission was associated with a higher risk of one-year mortality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazards regression analyses of the association between creatinine variability and the risk of deceased within a year from discharge.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCase (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e of Cr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,964 (18.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,019 (19.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.97\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.05\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10 (1.03\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,134 (20.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.04\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (1.16\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21 (1.14\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,933 (27.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72 (1.62\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75 (1.65\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62 (1.52\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-trend\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCV\u003c/b\u003e \u003cb\u003eof Cr.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,830 (17.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,975 (18.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.04\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18 (1.11\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (1.09\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,191 (20.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (1.20\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38 (1.30\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 (1.25\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,054 (29.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02 (1.91\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99 (1.87\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.77 (1.67\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-trend\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 was adjusted for sex, age, cancer type;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 was further adjusted for comorbidity index (\u0026le;\u0026thinsp;0, 1\u0026ndash;4, 5\u0026ndash;8, \u0026ge;\u0026thinsp;9), days of hospitalization, renal injury (yes/no), chemoradiotherapy (radiotherapy, chemotherapy, immunotherapy, and other), operation (none, general anesthesia, local anesthesia), systolic blood pressure (mmHg), respiratory rate (per minute), pulse (per minute);\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3 was adjusted for the first value of Cr., other biomarkers (including alpha-fetoprotein, albumin, total bilirubin, white blood cell counts, glucose, total- and LDL-cholesterol) as categorical variables (low, normal, high) on the bases of model 2.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CI, confidence interval; Cr., creatinine; CV, coefficient of variation (standard deviation/mean); HR, hazard ratio; SD, standard deviation;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the variability of creatinine within the clinically normal range was also related to the outcome event, with HR 1.80 (95% CI, 1.67\u0026ndash;1.93) of \u003cem\u003eSD\u003c/em\u003e and 1.80 (95% CI, 1.68\u0026ndash;1.93) of \u003cem\u003eCV\u003c/em\u003e (quartile four \u003cem\u003evs.\u003c/em\u003e quartile one, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Having one or more values of creatinine out of the clinically normal range reduced the association between creatinine variability and one-year mortality, but the association remained statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Our findings remain valid after controlling for the measurement interval of serum creatinine for a 3-days period (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this sensitivity analysis, the HR of \u003cem\u003eSD\u003c/em\u003e and \u003cem\u003eCV\u003c/em\u003e were 1.57 (95% CI, 1.44\u0026ndash;1.71) and 1.73 (95% CI, 1.58\u0026ndash;1.88) in quartile four compared with quartile one, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStratified analysis according to whether has value out of the clinically normal range and the number of creatinine measurements.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIn the normal range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWith value out of normal range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e of Cr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,490 (16.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e457 (32.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,590 (17.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (1.07\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456 (32.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.93\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,718 (18.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.23\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e469 (32.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (1.01\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,247 (24.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80 (1.67\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e623 (44.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48 (1.28\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-trend\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCV\u003c/b\u003e \u003cb\u003eof Cr.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,448 (15.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e417 (29.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,550 (17.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.08\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e448 (31.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12 (0.98\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,729 (19.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.26\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e508 (35.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28 (1.12\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,318 (25.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80 (1.68\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e632 (44.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.49 (1.31\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-trend\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAdjusted for Model 3;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CI, confidence interval; Cr., creatinine; CV, coefficient of variation (standard deviation/mean); HR, hazard ratio; SD, standard deviation;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis using the first creatinine results in every 3-day period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCase (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e of Cr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e996 (19.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,002 (19.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.93\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.99\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.97\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,003 (19.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.94\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.04\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 (1.03\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,593 (30.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80 (1.66\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69 (1.56\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57 (1.44\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-trend\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCV\u003c/b\u003e \u003cb\u003eof Cr.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e896 (17.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (\u003cem\u003eRef.\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e968 (18.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.01\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18 (1.08\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (1.04\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,093 (21.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (1.17\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36 (1.25\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32 (1.20\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,637 (31.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14 (1.97\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95 (1.79\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73 (1.58\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-trend\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CI, confidence interval; Cr., creatinine; CV, coefficient of variation (standard deviation/mean); HR, hazard ratio; SD, standard deviation;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, stratified analyses according to tumor types were shown in \u003cb\u003eeFigure 1\u003c/b\u003e. Consistent with the main results, a positive association between creatinine variability and risk of one-year mortality was found among all but patients with renal carcinoma and esophageal cancer. Subgroup analyses by absolute changes of creatinine from the first to the last measurement were shown in \u003cb\u003eeTable 4\u003c/b\u003e; and sex, age, measurement times of creatinine, and whether had undergone chemoradiotherapy or surgery (\u003cb\u003eeFigures 2\u0026thinsp;\u0026minus;\u0026thinsp;1 and 2\u0026ndash;2\u003c/b\u003e); and sensitivity analyses (\u003cb\u003eeFigures 3\u0026thinsp;\u0026minus;\u0026thinsp;1 and 3\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/b\u003e) after excluding those with imputation data, renal failure, and hospitalization less than 10 days did not significantly change the conclusion.\u003c/p\u003e \u003cp\u003eFinally, we also investigated the association between the mean and first creatinine test and mortality within one year. The results were shown in \u003cb\u003eeTables 5 and 6, and eFigures 4\u0026thinsp;\u0026minus;\u0026thinsp;1, 4\u0026thinsp;\u0026minus;\u0026thinsp;2, 5\u0026thinsp;\u0026minus;\u0026thinsp;1, and 5\u0026thinsp;\u0026minus;\u0026thinsp;2.\u003c/b\u003e These results were completely distinct from the variability of creatinine, that is a U-shape association between the mean/first creatinine and the risk of one-year mortality.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study enrolled a large sample of 41,911 patients with diverse cancer and demonstrated that higher serum creatinine fluctuation during hospital admission was associated with a higher mortality risk within one year. Notably, fluctuation of creatinine was associated with mortality regardless of whether all the values were within the clinically normal range or not. Furthermore, stratified analyses according to sex, age, measurement times of creatinine, and whether had undergone chemoradiotherapy or surgery did not significantly change the results. Sensitivity analyses also confirm the robustness of the conclusion.\u003c/p\u003e \u003cp\u003eIn previous studies, researchers evaluated the predictive value of absolute or relative changes in creatinine on mortality and illustrated that changes in serum creatinine identified the patients who were at increased risk for short- and long-term death.\u003csup\u003e29\u0026ndash;36\u003c/sup\u003e Our findings extend and clarify those of previous studies and extrapolated the conclusion to patients with cancer. Moreover, some existing observations reached conflicting conclusions. For example, they found that cancer patients are at higher risk of death with increases in serum creatinine or AKI.\u003csup\u003e35,37\u0026ndash;40\u003c/sup\u003e In contrast, an increase in serum creatinine was not associated with hematological adverse events in patients with breast cancer.\u003csup\u003e36\u003c/sup\u003e In addition, one study based on the UK biobank illustrated that creatinine-cystatin C was associated with neither cancer incidence nor the risk of cancer death. \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious findings aimed to elucidate the influence of the baseline value or absolute change of serum creatinine but not the overall variability of it on mortality for patients with cancer. However, the fluctuation may present a renal adaptability metric, a concept not measured by one value or absolute change. Our findings compensated for this gap and highlight the usefulness of serum creatinine fluctuation in at-risk cancer patients, allowing for risk stratification of those individuals to lower cancer mortality.\u003c/p\u003e \u003cp\u003eCancer prognosis is of keen interest to cancer patients, their caregivers, and providers owing to the high mortality rates.\u003csup\u003e41,42\u003c/sup\u003e An increasing body of research has focused on developing prognostic tools to guide patient-physician communication and decision-making. Until now, more than thirty interactive cancer prognostic tools have been developed, which incorporate cancer, demographic, and genetic characteristics, co-morbidities and therapy information, and modifiable risk factors.\u003csup\u003e27,28\u003c/sup\u003e However, these prognostic tools largely fail to account for biomarker changes over time, such as the variability of creatinine. Our study sheds new light on the development of new cancer prognostic tools which take the variability of biomarkers into account.\u003c/p\u003e \u003cp\u003eOur study showed robust associations between creatinine fluctuation and poor mortality outcomes in almost all subgroups. However, what is unexpected is the negative association among patients with renal carcinoma. A potential explanation may be the small sample size, especially since the case numbers were rather low, which vary in size from seven to dozens in each group. Confounding factors including drug therapies and treatment to prevent kidney disease progression are other possible reasons. This suggests that further research is needed to fully understand the relationship between serum creatinine variability and one-year mortality in renal carcinoma patients.\u003c/p\u003e \u003cp\u003eThe present study was not designed to explore the mechanism, but several potential explanations may explain these results. First, the variability of serum creatinine is usually ascribed to changes in GFR and reflects the capacity to adapt to nephrotoxic insults.\u003csup\u003e43\u003c/sup\u003e Elevated change of GFR means poor ability in maintaining renal homeostasis and is independently associated with all-cause mortality.\u003csup\u003e10,43\u003c/sup\u003e Second, creatinine generation rate is another important indicator for the variability of serum creatinine. Both the loss of muscle mass and poor nutrition can induce the change in creatinine generation rate,\u003csup\u003e44\u003c/sup\u003e and they are also strong risk factors for cancer mortality.\u003csup\u003e45\u003c/sup\u003e Finally, similar to AKI, a larger creatinine fluctuation frequently occurs in patients with tumors caused by chemotherapy-associated nephrotoxicity,\u003csup\u003e46\u003c/sup\u003e which always indicates an advanced stage of cancer. Despite the mechanisms under the association between creatinine fluctuation and mortality are not well understood, we believe that this finding has important clinical implications for the identification of high-risk cancer patients.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, we did not include any information about drug therapy during hospitalization. These pharmacologic strategies hold great promise to impact the variability of creatinine during hospitalization. However, given that all participants were patients with cancer, we performed the subgroup analysis based on cancer type, which accounted for some degrees of the confounding of treatment. Second, limited by the availability and accessibility of relevant data, the primary outcome was only one-year mortality without a longer follow-up. But one-year mortality is a commonly used endpoint in cancer research and is often considered clinically meaningful, for example, identifying patients who may require closer monitoring during hospitalization. Third, we did not define AKI based on the KDIGO AKI definition given the heterogeneity and high missing rate of urine output. A previous study conducted by Bhatraju1 et.al demonstrated that patients with different AKI sub-phenotypes defined by creatinine trajectory had different risks for mortality.\u003csup\u003e32\u003c/sup\u003e Therefore, we performed a subgroup analysis according to the degree of creatinine variability and renal failure defined by the ICD code to address the effect of kidney status. Fourth, patients with more advanced cancer are associated with a longer hospital stay and larger measurement times of creatinine, which could induce larger creatinine fluctuation. So, the potential effects of reverse causality cannot be excluded. Fifth, it is not possible to impute causality or provide a deeper mechanistic investigation due to the observational study design of this study. Instead, our findings provided exciting novel clues awaiting now in-depth analysis in follow-up studies. Finally, only Chinese patients were involved, which may limit the generalizability of these findings to other populations.\u003c/p\u003e \u003cp\u003eDespite these limitations, the results of this study have the potential to inform clinical practice by providing important information on the role of creatinine variability in the care of cancer patients. Given this finding, it is important for healthcare providers to monitor creatinine levels closely in patients with malignancy to identify fluctuations and intervene early to mitigate the risk of death. Further, new strategies considering the variability of creatinine for monitoring and mitigating the risk of adverse outcomes for cancer patients could be developed. Collectively, the relationship between blood creatinine fluctuations and the risk of death in patients with malignancy is complex and requires ongoing research and monitoring.\u003c/p\u003e \u003cp\u003eIn aggregate, the study highlights the importance of monitoring and controlling serum creatinine levels in cancer patients during hospital admission to reduce the risk of mortality. And provides important information on developing prevention strategies and triage decisions, and improves the survival of patients with cancer. Above all, our observations have important clinical implications and call for further research in this area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThis research has been done using the electronic medical records of eight hospitals in China and linked with the Chinese National Center for Disease Control and Prevention (CDC) Surveillance Points System. The authors thank all of the participants and staff of the eight hospitals and China CDC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eLin Li and Zhihui Li designed the work; Lin Li contributed to the acquisition of the data set; Lin Li, Huanhuan Yang, and Yi Zhang analyzed the data, completed the tables and figures, and are in charge of the interpretation of data; Huanhuan Yang drafted the manuscript; all other authors reviewed the work critically for important intellectual content; and all authors finally approved the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Support:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China (grant number 72274211), and the Research Fund, Vanke School of Public Health, Tsinghua University (grant number 2022BH012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Disclosures:\u003c/strong\u003e The authors have no conflicts of interest to report.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFerlay JEM, Lam F, Colombet M, Mery L, Pi\u0026ntilde;eros M et al. Global Cancer Observatory: Cancer Today. Lyon: International Agency for Research on Cancer; 2020 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr/today\u003c/span\u003e\u003cspan address=\"https://gco.iarc.fr/today\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed February 2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung CY, Kim HW, Han SH, et al. Creatinine-cystatin C ratio and mortality in cancer patients: a retrospective cohort study. J Cachexia Sarcopenia Muscle Aug. 2022;13(4):2064\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcsm.13006\u003c/span\u003e\u003cspan address=\"10.1002/jcsm.13006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLees JS, Ho F, Parra-Soto S, et al. Kidney function and cancer risk: An analysis using creatinine and cystatin C in a cohort study. EClinicalMedicine Aug. 2021;38:101030. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eclinm.2021.101030\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2021.101030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin ASP, Bilous RW, Coresh J, De Francisco ALM, De Jong PE, et al. Kidney disease: Improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Supplements. 2013;3(1):1\u0026ndash;150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/kisup.2012.73\u003c/span\u003e\u003cspan address=\"10.1038/kisup.2012.73\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerkins RM, Tang X, Bengier AC, Kirchner HL, Bucaloiu ID. Variability in estimated glomerular filtration rate is an independent risk factor for death among patients with stage 3 chronic kidney disease. Kidney Int Dec. 2012;82(12):1332\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ki.2012.281\u003c/span\u003e\u003cspan address=\"10.1038/ki.2012.281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoca SG, Singanamala S, Parikh CR. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int Mar. 2012;81(5):442\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ki.2011.379\u003c/span\u003e\u003cspan address=\"10.1038/ki.2011.379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellomo R, Kellum JA, Ronco C. Acute kidney injury. Lancet Aug 25. 2012;380(9843):756\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(11)61454-2\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(11)61454-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiew ED, Ware LB, Ikizler TA. Biological markers of acute kidney injury. J Am Soc Nephrol. May 2011;22(5):810\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1681/asn.2010080796\u003c/span\u003e\u003cspan address=\"10.1681/asn.2010080796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoca SG, Peixoto AJ, Garg AX, Krumholz HM, Parikh CR. The prognostic importance of a small acute decrement in kidney function in hospitalized patients: a systematic review and meta-analysis. Am J Kidney Dis Nov. 2007;50(5):712\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.ajkd.2007.07.018\u003c/span\u003e\u003cspan address=\"10.1053/j.ajkd.2007.07.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurin TC, Coresh J, Tonelli M, et al. Change in the estimated glomerular filtration rate over time and risk of all-cause mortality. Kidney Int Apr. 2013;83(4):684\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ki.2012.443\u003c/span\u003e\u003cspan address=\"10.1038/ki.2012.443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKao SS, Kim SW, Horwood CM, et al. Variability in inpatient serum creatinine: its impact upon short- and long-term mortality. Qjm Oct. 2015;108(10):781\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/qjmed/hcv020\u003c/span\u003e\u003cspan address=\"10.1093/qjmed/hcv020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt SF, Rohm M, Herzig S, Berriel Diaz M. Cancer Cachexia: More Than Skeletal Muscle Wasting. Trends Cancer Dec. 2018;4(12):849\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.trecan.2018.10.001\u003c/span\u003e\u003cspan address=\"10.1016/j.trecan.2018.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrado CM, Lieffers JR, McCargar LJ, et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol Jul. 2008;9(7):629\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1470-2045(08)70153-0\u003c/span\u003e\u003cspan address=\"10.1016/s1470-2045(08)70153-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosner MH, Perazella MA. Acute kidney injury in the patient with cancer. Kidney Res Clin Pract Sep. 2019;30(3):295\u0026ndash;308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.23876/j.krcp.19.042\u003c/span\u003e\u003cspan address=\"10.23876/j.krcp.19.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHumphreys BD, Soiffer RJ, Magee CC. Renal Failure Associated with Cancer and Its Treatment: An Update. J Am Soc Nephrol. 2005;16(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapoor M, Chan GZ. Jul. Malignancy and renal disease. \u003cem\u003eCrit Care Clin\u003c/em\u003e. 2001;17(3):571\u0026thinsp;\u0026ndash;\u0026thinsp;98, viii. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0749-0704(05)70199-8\u003c/span\u003e\u003cspan address=\"10.1016/s0749-0704(05)70199-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosolem MM, Rabello LS, Lisboa T, et al. Critically ill patients with cancer and sepsis: clinical course and prognostic factors. J Crit Care Jun. 2012;27(3):301\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcrc.2011.06.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jcrc.2011.06.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLameire N, Vanholder R, Van Biesen W, Benoit D. Acute kidney injury in critically ill cancer patients: an update. Crit Care Aug 2. 2016;20(1):209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13054-016-1382-6\u003c/span\u003e\u003cspan address=\"10.1186/s13054-016-1382-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosner MH, Perazella MA. Acute Kidney Injury in Patients with Cancer. N Engl J Med. May 2017;4(18):1770\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMra1613984\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra1613984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Bu P. The two sides of creatine in cancer. Trends Cell Biol May. 2022;32(5):380\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tcb.2021.11.004\u003c/span\u003e\u003cspan address=\"10.1016/j.tcb.2021.11.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKazak L, Cohen P. Creatine metabolism: energy homeostasis, immunity and cancer biology. Nat Rev Endocrinol Aug. 2020;16(8):421\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41574-020-0365-5\u003c/span\u003e\u003cspan address=\"10.1038/s41574-020-0365-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVander Heiden MG, DeBerardinis RJ. Understanding the Intersections between Metabolism and Cancer Biology. \u003cem\u003eCell\u003c/em\u003e. Feb 9. 2017;168(4):657\u0026ndash;669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2016.12.039\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2016.12.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooper R, Naclerio F, Allgrove J, Jimenez A. Creatine supplementation with specific view to exercise/sports performance: an update. J Int Soc Sports Nutr Jul. 2012;20(1):33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1550-2783-9-33\u003c/span\u003e\u003cspan address=\"10.1186/1550-2783-9-33\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurmi K, Hitosugi S, Yu J, et al. Tyrosine Phosphorylation of Mitochondrial Creatine Kinase 1 Enhances a Druggable Tumor Energy Shuttle Pathway. Cell Metab Dec. 2018;4(6):833\u0026ndash;e8478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2018.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2018.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFenouille N, Bassil CF, Ben-Sahra I, et al. The creatine kinase pathway is a metabolic vulnerability in EVI1-positive acute myeloid leukemia. Nat Med. Mar 2017;23(3):301\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nm.4283\u003c/span\u003e\u003cspan address=\"10.1038/nm.4283\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng C, Wang E, Li JS, et al. Serum creatinine/cystatin C ratio as a screening tool for sarcopenia and prognostic indicator for patients with esophageal cancer. BMC Geriatr Mar. 2022;15(1):207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12877-022-02925-8\u003c/span\u003e\u003cspan address=\"10.1186/s12877-022-02925-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabin BA, Gaglio B, Sanders T, et al. Predicting cancer prognosis using interactive online tools: a systematic review and implications for cancer care providers. Cancer Epidemiol Biomarkers Prev Oct. 2013;22(10):1645\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1055-9965.Epi-13-0513\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.Epi-13-0513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeow H, Tanuseputro P, Barbera L, et al. Development and Validation of a Prognostic Survival Model With Patient-Reported Outcomes for Patients With Cancer. JAMA Netw Open Apr. 2020;1(4):e201768. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2020.1768\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2020.1768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoca SG, Zabetian A, Ferket BS, et al. Evaluation of Short-Term Changes in Serum Creatinine Level as a Meaningful End Point in Randomized Clinical Trials. J Am Soc Nephrol Aug. 2016;27(8):2529\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1681/asn.2015060642\u003c/span\u003e\u003cspan address=\"10.1681/asn.2015060642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKao SS, Kim SW, Horwood CM, et al. Variability in inpatient serum creatinine: its impact upon short- and long-term mortality. QJM: Int J Med. 2015;108(10):781\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/qjmed/hcv020\u003c/span\u003e\u003cspan address=\"10.1093/qjmed/hcv020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorenkevych D, Ozrazgat-Baslanti T, Thottakkara P, et al. The Pattern of Longitudinal Change in Serum Creatinine and 90-Day Mortality After Major Surgery. Ann Surg Jun. 2016;263(6):1219\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/sla.0000000000001362\u003c/span\u003e\u003cspan address=\"10.1097/sla.0000000000001362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatraju PK, Mukherjee P, Robinson-Cohen C, et al. Acute kidney injury subphenotypes based on creatinine trajectory identifies patients at increased risk of death. Crit Care Nov. 2016;17(1):372. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13054-016-1546-4\u003c/span\u003e\u003cspan address=\"10.1186/s13054-016-1546-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarlo KG, Bates DW, Seger DL, Fiskio JM, Charytan DM. Association of Changes in Creatinine and Potassium Levels After Initiation of Renin Angiotensin Aldosterone System Inhibitors With Emergency Department Visits, Hospitalizations, and Mortality in Individuals With Chronic Kidney Disease. JAMA Netw Open Nov. 2018;2(7):e183874. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2018.3874\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2018.3874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCullaro G, Hsu CY, Lai JC. Variability in serum creatinine is associated with waitlist and post-liver transplant mortality in patients with cirrhosis. Hepatol Oct. 2022;76(4):1069\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.32497\u003c/span\u003e\u003cspan address=\"10.1002/hep.32497\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuels J, Ng CS, Nates J, et al. Small increases in serum creatinine are associated with prolonged ICU stay and increased hospital mortality in critically ill patients with cancer. Support Care Cancer Oct. 2011;19(10):1527\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00520-010-0978-7\u003c/span\u003e\u003cspan address=\"10.1007/s00520-010-0978-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaeda A, Irie K, Hashimoto N, et al. Serum concentration of the CKD4/6 inhibitor abemaciclib, but not of creatinine, strongly predicts hematological adverse events in patients with breast cancer: a preliminary report. Invest New Drugs Feb. 2021;39(1):272\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10637-020-00994-3\u003c/span\u003e\u003cspan address=\"10.1007/s10637-020-00994-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC\u0026oacute;rdova-S\u0026aacute;nchez BM, Herrera-G\u0026oacute;mez \u0026Aacute;, \u0026Ntilde;amendys-Silva SA. Acute Kidney Injury Classified by Serum Creatinine and Urine Output in Critically Ill Cancer Patients. Biomed Res Int. 2016;2016:6805169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2016/6805169\u003c/span\u003e\u003cspan address=\"10.1155/2016/6805169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeylanova N, Crichton S, Zhang J, Fisher R, Ostermann M. Acute kidney injury in critically ill cancer patients is associated with mortality: A retrospective analysis. PLoS ONE. 2020;15(5):e0232370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0232370\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0232370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang E, Park M, Park PG, et al. Acute kidney injury predicts all-cause mortality in patients with cancer. Cancer Med Jun. 2019;8(6):2740\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.2140\u003c/span\u003e\u003cspan address=\"10.1002/cam4.2140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlagelse C, Gammelager H, Iversen LH, S\u0026oslash;rensen HT, Christiansen CF. Acute kidney injury and 1-year mortality after colorectal cancer surgery: a population-based cohort study. BMJ Open Mar. 2019;13(3):e024817. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2018-024817\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2018-024817\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bock GH, Bonnema J, Zwaan RE, et al. Patient's needs and preferences in routine follow-up after treatment for breast cancer. Br J Cancer Mar. 2004;22(6):1144\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/sj.bjc.6601655\u003c/span\u003e\u003cspan address=\"10.1038/sj.bjc.6601655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutten LJ, Arora NK, Bakos AD, Aziz N, Rowland J. Information needs and sources of information among cancer patients: a systematic review of research (1980\u0026ndash;2003). Patient Educ Couns Jun. 2005;57(3):250\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pec.2004.06.006\u003c/span\u003e\u003cspan address=\"10.1016/j.pec.2004.06.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Aly Z, Balasubramanian S, McDonald JR, Scherrer JF, O'Hare AM. Greater variability in kidney function is associated with an increased risk of death. Kidney Int Dec. 2012;82(11):1208\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ki.2012.276\u003c/span\u003e\u003cspan address=\"10.1038/ki.2012.276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovesdy CP, George SM, Anderson JE, Kalantar-Zadeh K. Outcome predictability of biomarkers of protein-energy wasting and inflammation in moderate and advanced chronic kidney disease. Am J Clin Nutr Aug. 2009;90(2):407\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3945/ajcn.2008.27390\u003c/span\u003e\u003cspan address=\"10.3945/ajcn.2008.27390\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson FP, Sheehan JM, Mariani LH, Berns JS. Creatinine generation is reduced in patients requiring continuous venovenous hemodialysis and independently predicts mortality. Nephrol Dial Transpl Nov. 2012;27(11):4088\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ndt/gfr809\u003c/span\u003e\u003cspan address=\"10.1093/ndt/gfr809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitchlu A, McArthur E, Amir E, et al. Acute Kidney Injury in Patients Receiving Systemic Treatment for Cancer: A Population-Based Cohort Study. J Natl Cancer Inst Jul. 2019;1(7):727\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jnci/djy167\u003c/span\u003e\u003cspan address=\"10.1093/jnci/djy167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer, Creatinine, Mortality, Variability","lastPublishedDoi":"10.21203/rs.3.rs-4639262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4639262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCreatinine variability has a close and reciprocal relationship with cancer risk. However, the role of creatinine variability on mortality among cancer patients remains unclear. Thus, the objective here is to fill this gap.\u003cstrong\u003e \u003c/strong\u003eWe conducted a multi-center study including all patients with solid tumors admitted to eight hospitals in China between January 1, 2013, and December 31, 2019, on their primary admission. The variability of blood creatinine was evaluated by the standard deviation\u003cem\u003e (SD)\u003c/em\u003e and coefficient of variation\u003cem\u003e(CV)\u003c/em\u003e. All deaths and causes of death were identified from the Chinese National Center for Disease Control and Prevention (CDC) Surveillance Points System. Analyses were constructed by multiple Cox regression models.\u003cstrong\u003e \u003c/strong\u003eThe study comprised a total of 41,911 cancer patients, of which 9,050 events were observed. Higher serum creatinine fluctuation was associated with an elevated risk of one-year mortality significantly, with a hazard ratio of 1.62 (95% confidence interval, 1.52-1.72; \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001) for the standard deviation of creatinine in quartile four compared with quartile one. Furthermore, the association persisted even though all creatinine was within the clinically normal range. The coefficient of variation of creatinine showed similar results. Higher serum creatinine fluctuation during hospital admission is associated with an elevated risk of one-year mortality among cancer patients, even if the fluctuation is within the clinically normal range.\u003c/p\u003e","manuscriptTitle":"Association of serum creatinine variability and risk of 1-year mortality among patients with cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 01:57:38","doi":"10.21203/rs.3.rs-4639262/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"783872d4-3422-430c-b809-6fc3361a69c8","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-04T13:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 01:57:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4639262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4639262","identity":"rs-4639262","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}